A group of Cornell physicists and computer scientists developed an unsupervised machine learning method called X-ray diffraction temperature clustering (X-TEC). This method can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from high-volume X-ray diffraction measurements taken at various temperatures. Using X-TEC, the researchers studied the major components of a pyrochlore oxide metal, Cd2Re2O7.
Their paper, published in the Proceedings of the National Academy of Sciences, demonstrates that machine learning can generate a fair and thorough analysis of such data that combines long-range and short-range structural correlations as a function of temperature.
The researchers believe that the atomic-scale understanding of fluctuations in a complicated quantum substance will pave paths for more scientific discoveries of new phases of matter by employing extensive, information-rich diffraction data.
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